For the past decade and continuing today, Naval Surface Warfare Center, Port Hueneme Division (NSWCPHD) has been in a perpetual state of emergency. Inaccurate forecasting for future contract actions contained in our Long Range Acquisition Forecast (LRAF) has left the NSWC PHD acquisition workforce unprepared to face increasing demands. As a result, more pending contract actions are unknown until the need date arrives and an emergency effort must be initiated to complete the action. This emergent trend can be found throughout the Department of the Navy (DON). It is imperative that a more accurate forecasting model be utilized within NSWCPHD to capture the demand signal of the acquisition workforce. This thesis reviewed the policy and procedures of the U.S. federal government including those within the Department of Defense (DOD), DON and Navy Sea Systems Command (NAVSEA), and NSWC PHD to understand the procedure for capturing the demand for future contract actions. This research found that the current methodology in place for the LRAF is heavily dependent on the requirement generator and historical references, which do not cover all contracting data at NSWC PHD. This paper identified a path for the acquisition workforce to incorporate data-driven analytics to its forecasting models to more accurately represent demand for that workforce. This research begins the process of moving toward the data-driven forecasting mentioned and determining the first steps forward.